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Cited by in F6Publishing
For: Erfanian N, Heydari AA, Iañez P, Derakhshani A, Ghasemigol M, Farahpour M, Nasseri S, Safarpour H, Sahebkar A. Deep Learning Applications in Single-Cell Omics Data Analysis.. [DOI: 10.1101/2021.11.26.470166] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Chen J, Xu H, Tao W, Chen Z, Zhao Y, Han JJ. Transformer for one stop interpretable cell type annotation. Nat Commun 2023;14:223. [PMID: 36641532 DOI: 10.1038/s41467-023-35923-4] [Reference Citation Analysis]
2 Brombacher E, Hackenberg M, Kreutz C, Binder H, Treppner M. The performance of deep generative models for learning joint embeddings of single-cell multi-omics data. Front Mol Biosci 2022;9. [DOI: 10.3389/fmolb.2022.962644] [Reference Citation Analysis]
3 Brombacher E, Hackenberg M, Kreutz C, Binder H, Treppner M. The performance of deep generative models for learning joint embeddings of single-cell multi-omics data.. [DOI: 10.1101/2022.06.06.494951] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Heydari AA, Davalos OA, Hoyer KK, Sindi SS. N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification.. [DOI: 10.1101/2022.05.12.491682] [Reference Citation Analysis]
5 Allen C, Chang Y, Ma Q, Chung D. MAPLE: A Hybrid Framework for Multi-Sample Spatial Transcriptomics Data.. [DOI: 10.1101/2022.02.28.482296] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
6 Heydari AA, Sindi SS. Deep Learning in Spatial Transcriptomics: Learning From the Next Next-Generation Sequencing.. [DOI: 10.1101/2022.02.28.482392] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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